class_docstr: The main class of the dynamic linear model.
  
  
  This is the main class of the Bayeisan dynamic linear model.
  It provides the modeling, filtering, forecasting and smoothing
  function of a dlm.
  The dlm use the @builder to construct the @baseModel based on user supplied
  @components and then run @kalmanFilter to filter the result.
  
  Example 1:
      >>> # randomly generate fake data on 1000 days
      >>> import numpy as np
      >>> data = np.random.random((1, 1000))
      >>> # construct the dlm of a linear trend and a 7-day seasonality
      >>> myDlm = dlm(data) + trend(degree = 2, 0.98) + seasonality(period = 7, 0.98)
      >>> # filter the result
      >>> myDlm.fitForwardFilter()
      >>> # extract the filtered result
      >>> myDlm.getFilteredObs()
  
  Example 2 (fit a linear regression):
      >>> from pydlm import dynamic
      >>> data = np.random.random((1, 100))
      >>> mydlm = dlm(data) + trend(degree=1, 0.98, name='a') +
                      dynamic(features=[[i] for i in range(100)], 1, name='b')
      >>> mydlm.fit()
      >>> coef_a = mydlm.getLatentState('a')
      >>> coef_b = mydlm.getLatentState('b')
  
  Attributes:
     data: a list of doubles of the raw time series data.
           It could be either the python's built-in list of
           doubles or numpy 1d array.
code: def __init__(self, data, **options):
      super(dlm, self).__init__(data, **options)
  
      # This model is used for prediction. Prediction functions
      # will change the model status to forecast at a particular
      # date. Using a copied model will help the main model from
      # being changed and behaving abnormally.
      self._predictModel = None
  
imports: from copy import deepcopy
  from numpy import matrix
  from pydlm.predict.dlmPredictMod import dlmPredictModule
  from pydlm.access.dlmAccessMod import dlmAccessModule
  from pydlm.tuner.dlmTuneMod import dlmTuneModule
  from pydlm.plot.dlmPlotMod import dlmPlotModule
label_desc: Use image node_id 1 to create a new dlm object from inherited base classes: dlmPlotModule, dlmPredictModule, dlmAccessModule, dlmTuneModule with example: obj = dlm(data)